{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimun Legal Drinking Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from dowhy import CausalModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agecell</th>\n",
       "      <th>all</th>\n",
       "      <th>allfitted</th>\n",
       "      <th>internal</th>\n",
       "      <th>internalfitted</th>\n",
       "      <th>external</th>\n",
       "      <th>externalfitted</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>alcoholfitted</th>\n",
       "      <th>homicide</th>\n",
       "      <th>homicidefitted</th>\n",
       "      <th>suicide</th>\n",
       "      <th>suicidefitted</th>\n",
       "      <th>mva</th>\n",
       "      <th>mvafitted</th>\n",
       "      <th>drugs</th>\n",
       "      <th>drugsfitted</th>\n",
       "      <th>externalother</th>\n",
       "      <th>externalotherfitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.068493</td>\n",
       "      <td>92.825401</td>\n",
       "      <td>91.706146</td>\n",
       "      <td>16.617590</td>\n",
       "      <td>16.738131</td>\n",
       "      <td>76.207817</td>\n",
       "      <td>74.968010</td>\n",
       "      <td>0.639138</td>\n",
       "      <td>0.794344</td>\n",
       "      <td>16.316818</td>\n",
       "      <td>16.284573</td>\n",
       "      <td>11.203714</td>\n",
       "      <td>11.592100</td>\n",
       "      <td>35.829327</td>\n",
       "      <td>34.817780</td>\n",
       "      <td>3.872425</td>\n",
       "      <td>3.448835</td>\n",
       "      <td>8.534373</td>\n",
       "      <td>8.388236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.150684</td>\n",
       "      <td>95.100739</td>\n",
       "      <td>91.883720</td>\n",
       "      <td>18.327684</td>\n",
       "      <td>16.920654</td>\n",
       "      <td>76.773056</td>\n",
       "      <td>74.963066</td>\n",
       "      <td>0.677409</td>\n",
       "      <td>0.837575</td>\n",
       "      <td>16.859964</td>\n",
       "      <td>16.270697</td>\n",
       "      <td>12.193368</td>\n",
       "      <td>11.593611</td>\n",
       "      <td>35.639256</td>\n",
       "      <td>34.633888</td>\n",
       "      <td>3.236511</td>\n",
       "      <td>3.470022</td>\n",
       "      <td>8.655786</td>\n",
       "      <td>8.530174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.232876</td>\n",
       "      <td>92.144295</td>\n",
       "      <td>92.049065</td>\n",
       "      <td>18.911053</td>\n",
       "      <td>17.098843</td>\n",
       "      <td>73.233238</td>\n",
       "      <td>74.950226</td>\n",
       "      <td>0.866443</td>\n",
       "      <td>0.877835</td>\n",
       "      <td>15.219254</td>\n",
       "      <td>16.262882</td>\n",
       "      <td>11.715812</td>\n",
       "      <td>11.595129</td>\n",
       "      <td>34.205650</td>\n",
       "      <td>34.446735</td>\n",
       "      <td>3.202071</td>\n",
       "      <td>3.492069</td>\n",
       "      <td>8.513741</td>\n",
       "      <td>8.662681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.315069</td>\n",
       "      <td>88.427757</td>\n",
       "      <td>92.202141</td>\n",
       "      <td>16.101770</td>\n",
       "      <td>17.272680</td>\n",
       "      <td>72.325981</td>\n",
       "      <td>74.929466</td>\n",
       "      <td>0.867308</td>\n",
       "      <td>0.915115</td>\n",
       "      <td>16.742825</td>\n",
       "      <td>16.261148</td>\n",
       "      <td>11.275010</td>\n",
       "      <td>11.596655</td>\n",
       "      <td>32.278957</td>\n",
       "      <td>34.256302</td>\n",
       "      <td>3.280689</td>\n",
       "      <td>3.514980</td>\n",
       "      <td>8.258285</td>\n",
       "      <td>8.785728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.397261</td>\n",
       "      <td>88.704941</td>\n",
       "      <td>92.342918</td>\n",
       "      <td>17.363520</td>\n",
       "      <td>17.442156</td>\n",
       "      <td>71.341415</td>\n",
       "      <td>74.900757</td>\n",
       "      <td>1.019163</td>\n",
       "      <td>0.949407</td>\n",
       "      <td>14.947726</td>\n",
       "      <td>16.265511</td>\n",
       "      <td>10.984314</td>\n",
       "      <td>11.598189</td>\n",
       "      <td>32.650967</td>\n",
       "      <td>34.062588</td>\n",
       "      <td>3.548198</td>\n",
       "      <td>3.538755</td>\n",
       "      <td>8.417533</td>\n",
       "      <td>8.899288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     agecell        all  allfitted   internal  internalfitted   external  \\\n",
       "0  19.068493  92.825401  91.706146  16.617590       16.738131  76.207817   \n",
       "1  19.150684  95.100739  91.883720  18.327684       16.920654  76.773056   \n",
       "2  19.232876  92.144295  92.049065  18.911053       17.098843  73.233238   \n",
       "3  19.315069  88.427757  92.202141  16.101770       17.272680  72.325981   \n",
       "4  19.397261  88.704941  92.342918  17.363520       17.442156  71.341415   \n",
       "\n",
       "   externalfitted   alcohol  alcoholfitted   homicide  homicidefitted  \\\n",
       "0       74.968010  0.639138       0.794344  16.316818       16.284573   \n",
       "1       74.963066  0.677409       0.837575  16.859964       16.270697   \n",
       "2       74.950226  0.866443       0.877835  15.219254       16.262882   \n",
       "3       74.929466  0.867308       0.915115  16.742825       16.261148   \n",
       "4       74.900757  1.019163       0.949407  14.947726       16.265511   \n",
       "\n",
       "     suicide  suicidefitted        mva  mvafitted     drugs  drugsfitted  \\\n",
       "0  11.203714      11.592100  35.829327  34.817780  3.872425     3.448835   \n",
       "1  12.193368      11.593611  35.639256  34.633888  3.236511     3.470022   \n",
       "2  11.715812      11.595129  34.205650  34.446735  3.202071     3.492069   \n",
       "3  11.275010      11.596655  32.278957  34.256302  3.280689     3.514980   \n",
       "4  10.984314      11.598189  32.650967  34.062588  3.548198     3.538755   \n",
       "\n",
       "   externalother  externalotherfitted  \n",
       "0       8.534373             8.388236  \n",
       "1       8.655786             8.530174  \n",
       "2       8.513741             8.662681  \n",
       "3       8.258285             8.785728  \n",
       "4       8.417533             8.899288  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_stata(\n",
    "    \"../data/mlda.dta\"\n",
    ")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all: All deaths\n",
    "\n",
    "# centralize\n",
    "data['age'] = data['agecell'] - 21\n",
    "\n",
    "# treatment variable\n",
    "data['over21'] = (data['age'] >=0).astype(int)\n",
    "\n",
    "# other covariates in Regression Discontinuity Desgins\n",
    "data['age2'] = data['age'].apply(lambda x: x**2)\n",
    "data['over_age'] = data['age'] * data['over21']\n",
    "data['over_age2'] = data['age2'] * data['over21']\n",
    "\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Regression with **Single Covariate**: \n",
    "\n",
    "$\\bar{M_a} = \\alpha + \\rho D_a + \\gamma a + \\epsilon_a$\n",
    "\n",
    "Regression with **Mulitple Covariate**: \n",
    "\n",
    "$\\bar{M_a} = \\alpha + \\rho D_a + \\gamma_1 a + \\gamma_2 a^2 + \\delta_1 aD_a + \\delta_2 a^2D_a + \\epsilon_a$\n",
    "\n",
    "In both equations above:\n",
    "* $a$: age, centralized around 21\n",
    "* $\\bar{M_a}$: is the average all-deaths death rate in age $a$\n",
    "* $D_a$: if $a$ exceeds 0 (in which the real age exceeds 21)\n",
    "\n",
    "In the following sections, we'll consider only the causal effect of minimum legal drinking age on **all-deaths death rate** with **multiple covariates**, which should be around 9.55 according to the table 4.1 in Angrist's book (row 1, column 2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DoWhy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dowhy_rd_model = CausalModel(\n",
    "    data=data,\n",
    "    treatment=\"over21\",\n",
    "    outcome=\"all\",\n",
    "    common_causes=['age', 'age2', 'over_age', 'over_age2'],\n",
    ")\n",
    "\n",
    "dowhy_rd_model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "    d                                        \n",
      "─────────(E[all|age,age2,over_age2,over_age])\n",
      "d[over₂₁]                                    \n",
      "Estimand assumption 1, Unconfoundedness: If U→{over21} and U→all then P(all|over21,age,age2,over_age2,over_age,U) = P(all|over21,age,age2,over_age2,over_age)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable(s) found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = dowhy_rd_model.identify_effect()\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "    d                                        \n",
      "─────────(E[all|age,age2,over_age2,over_age])\n",
      "d[over₂₁]                                    \n",
      "Estimand assumption 1, Unconfoundedness: If U→{over21} and U→all then P(all|over21,age,age2,over_age2,over_age,U) = P(all|over21,age,age2,over_age2,over_age)\n",
      "\n",
      "## Realized estimand\n",
      "b: all~over21+age+age2+over_age2+over_age\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 9.54778854390338\n",
      "p-value: [1.97256564e-05]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/davidli/anaconda3/envs/mastering-py-metrics/lib/python3.11/site-packages/dowhy/causal_estimators/regression_estimator.py:179: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  intercept_parameter = self.model.params[0]\n"
     ]
    }
   ],
   "source": [
    "causal_estimate_reg = dowhy_rd_model.estimate_effect(\n",
    "    identified_estimand,\n",
    "    method_name=\"backdoor.linear_regression\",\n",
    "    test_significance=True\n",
    ")\n",
    "\n",
    "print(causal_estimate_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `statsmodels` Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    all   R-squared:                       0.682\n",
      "Model:                            OLS   Adj. R-squared:                  0.644\n",
      "Method:                 Least Squares   F-statistic:                     18.02\n",
      "Date:                Mon, 08 Jan 2024   Prob (F-statistic):           1.62e-09\n",
      "Time:                        14:15:04   Log-Likelihood:                -104.57\n",
      "No. Observations:                  48   AIC:                             221.1\n",
      "Df Residuals:                      42   BIC:                             232.4\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     93.0729      1.404     66.301      0.000      90.240      95.906\n",
      "over21         9.5478      1.985      4.809      0.000       5.541      13.554\n",
      "age           -0.8306      3.290     -0.252      0.802      -7.470       5.809\n",
      "age2          -0.8403      1.615     -0.520      0.606      -4.100       2.419\n",
      "over_age      -6.0170      4.653     -1.293      0.203     -15.407       3.373\n",
      "over_age2      2.9042      2.284      1.271      0.211      -1.706       7.514\n",
      "==============================================================================\n",
      "Omnibus:                        0.040   Durbin-Watson:                   2.004\n",
      "Prob(Omnibus):                  0.980   Jarque-Bera (JB):                0.203\n",
      "Skew:                           0.049   Prob(JB):                        0.903\n",
      "Kurtosis:                       2.697   Cond. No.                         40.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "formula = \"\"\"\n",
    "all ~ over21 + age + age2 + over_age + over_age2\n",
    "\"\"\"\n",
    "\n",
    "rd_sm_model = smf.ols(\n",
    "    formula=formula,\n",
    "    data=data\n",
    ")\n",
    "\n",
    "rd_sm_results = rd_sm_model.fit()\n",
    "\n",
    "print(rd_sm_results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    all   R-squared:                       0.595\n",
      "Model:                            OLS   Adj. R-squared:                  0.577\n",
      "Method:                 Least Squares   F-statistic:                     32.99\n",
      "Date:                Mon, 08 Jan 2024   Prob (F-statistic):           1.51e-09\n",
      "Time:                        14:15:04   Log-Likelihood:                -110.41\n",
      "No. Observations:                  48   AIC:                             226.8\n",
      "Df Residuals:                      45   BIC:                             232.4\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     91.8414      0.805    114.083      0.000      90.220      93.463\n",
      "over21         7.6627      1.440      5.320      0.000       4.762      10.564\n",
      "age           -0.9747      0.632     -1.541      0.130      -2.249       0.299\n",
      "==============================================================================\n",
      "Omnibus:                        0.189   Durbin-Watson:                   1.617\n",
      "Prob(Omnibus):                  0.910   Jarque-Bera (JB):                0.194\n",
      "Skew:                           0.131   Prob(JB):                        0.907\n",
      "Kurtosis:                       2.831   Cond. No.                         6.07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "benchmark_results = smf.ols(\n",
    "    formula='all ~ over21 + age',\n",
    "    data=data\n",
    ").fit()\n",
    "\n",
    "print(benchmark_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the confidence interval of a given fitted OLS model\n",
    "def ols_predict_ci(\n",
    "        model: sm.regression.linear_model.RegressionResultsWrapper,\n",
    "        X: pd.DataFrame,\n",
    "        alpha: float = .05,\n",
    "    ) -> pd.DataFrame:\n",
    "    \"\"\"Get the prediction with confidence interval.\n",
    "\n",
    "    Args:\n",
    "        model (RegressionResultsWrapper): Fitted statsmodels OLS model.\n",
    "        X (pd.DataFrame): Values to predict.\n",
    "        alpha (float, optional): Statistical significance. Defaults to .05.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: Predictted results.\n",
    "    \"\"\"\n",
    "    if alpha < 0 or alpha > 1:\n",
    "        raise ValueError(\"Argument alpha must between 0 and 1\")\n",
    "    \n",
    "    predictions_df: pd.DataFrame = (\n",
    "        model\n",
    "        .get_prediction(X)\n",
    "        .summary_frame(alpha=alpha)\n",
    "    )\n",
    "\n",
    "    return predictions_df[['mean', 'mean_ci_lower', 'mean_ci_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>intercept</th>\n",
       "      <th>age</th>\n",
       "      <th>over21</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean_ci_lower</th>\n",
       "      <th>mean_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>93.790738</td>\n",
       "      <td>92.142116</td>\n",
       "      <td>95.439361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.995996</td>\n",
       "      <td>0</td>\n",
       "      <td>93.786836</td>\n",
       "      <td>92.142205</td>\n",
       "      <td>95.431467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.991992</td>\n",
       "      <td>0</td>\n",
       "      <td>93.782933</td>\n",
       "      <td>92.142288</td>\n",
       "      <td>95.423578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.987988</td>\n",
       "      <td>0</td>\n",
       "      <td>93.779030</td>\n",
       "      <td>92.142365</td>\n",
       "      <td>95.415696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.983984</td>\n",
       "      <td>0</td>\n",
       "      <td>93.775128</td>\n",
       "      <td>92.142436</td>\n",
       "      <td>95.407820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1</td>\n",
       "      <td>1.983984</td>\n",
       "      <td>1</td>\n",
       "      <td>97.570321</td>\n",
       "      <td>95.937629</td>\n",
       "      <td>99.203013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>1.987988</td>\n",
       "      <td>1</td>\n",
       "      <td>97.566418</td>\n",
       "      <td>95.929753</td>\n",
       "      <td>99.203084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1</td>\n",
       "      <td>1.991992</td>\n",
       "      <td>1</td>\n",
       "      <td>97.562515</td>\n",
       "      <td>95.921870</td>\n",
       "      <td>99.203161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>1.995996</td>\n",
       "      <td>1</td>\n",
       "      <td>97.558613</td>\n",
       "      <td>95.913982</td>\n",
       "      <td>99.203244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>97.554710</td>\n",
       "      <td>95.906087</td>\n",
       "      <td>99.203333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     intercept       age  over21       mean  mean_ci_lower  mean_ci_upper\n",
       "0            1 -2.000000       0  93.790738      92.142116      95.439361\n",
       "1            1 -1.995996       0  93.786836      92.142205      95.431467\n",
       "2            1 -1.991992       0  93.782933      92.142288      95.423578\n",
       "3            1 -1.987988       0  93.779030      92.142365      95.415696\n",
       "4            1 -1.983984       0  93.775128      92.142436      95.407820\n",
       "..         ...       ...     ...        ...            ...            ...\n",
       "995          1  1.983984       1  97.570321      95.937629      99.203013\n",
       "996          1  1.987988       1  97.566418      95.929753      99.203084\n",
       "997          1  1.991992       1  97.562515      95.921870      99.203161\n",
       "998          1  1.995996       1  97.558613      95.913982      99.203244\n",
       "999          1  2.000000       1  97.554710      95.906087      99.203333\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"intercept\": 1,\n",
    "    \"age\": np.linspace(-2.0, 2.0, num=1000),\n",
    "    \"over21\": (np.linspace(-2.0, 2.0, num=1000) > 0).astype(int),\n",
    "})\n",
    "\n",
    "prediction_data = pd.concat(\n",
    "    [df, ols_predict_ci(benchmark_results, df, .05)],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax: plt.Axes\n",
    "fig, ax = plt.subplots(figsize=(10, 6), dpi=200)\n",
    "\n",
    "for over21 in [0, 1]:\n",
    "    sns.scatterplot(\n",
    "        data=data[data['over21']==over21],\n",
    "        x=\"age\",\n",
    "        y=\"all\",\n",
    "        color='grey',\n",
    "    )\n",
    "\n",
    "    sns.lineplot(\n",
    "        data=prediction_data[prediction_data['over21']==over21],\n",
    "        x=\"age\",\n",
    "        y=\"mean\",\n",
    "        color='grey',\n",
    "    )\n",
    "\n",
    "    for boundary in [\"lower\", \"upper\"]:\n",
    "        sns.lineplot(\n",
    "            data=prediction_data[prediction_data['over21']==over21],\n",
    "            x=\"age\",\n",
    "            y=f\"mean_ci_{boundary}\",\n",
    "            linestyle=\":\",\n",
    "            color='black',\n",
    "        )\n",
    "\n",
    "ax.set_xticks(ticks=[-2, -1, 0, 1, 2], labels=[19, 20, 21, 22, 23])\n",
    "ax.set_ylim([80, 115])\n",
    "\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Death Rate From All Causes (per 100,000)\")\n",
    "ax.set_title(\"Regression Discontinuity on Minimum Legal Drinking Age \\n With 95% Confidence Interval\")\n",
    "\n",
    "plt.savefig(\"../figs/regplot.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mastering-py-metrics",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
